model.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from utils import *
import time
import numpy as np
import torch
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F


class WaveRNN(nn.Module):
    def __init__(self, hidden_size=896, quantisation=256):
        super(WaveRNN, self).__init__()

        self.hidden_size = hidden_size
        self.split_size = hidden_size // 2

        # The main hidden state matmul
        self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)

        # Output fc layers
        self.O1 = nn.Linear(self.split_size, self.split_size)
        self.O2 = nn.Linear(self.split_size, quantisation)
        self.O3 = nn.Linear(self.split_size, self.split_size)
        self.O4 = nn.Linear(self.split_size, quantisation)

        # Input fc layers
        self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
        self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)

        # biases for the gates
        self.bias_u = nn.Parameter(torch.zeros(self.hidden_size))
        self.bias_r = nn.Parameter(torch.zeros(self.hidden_size))
        self.bias_e = nn.Parameter(torch.zeros(self.hidden_size))

        # display num params
        self.print_stats()

    def forward(self, prev_y, prev_hidden, current_coarse):
        # Main matmul - the projection is split 3 ways
        R_hidden = self.R(prev_hidden)
        R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)

        # Project the prev input
        coarse_input_proj = self.I_coarse(prev_y)
        I_coarse_u, I_coarse_r, I_coarse_e = \
            torch.split(coarse_input_proj, self.split_size, dim=1)

        # Project the prev input and current coarse sample
        fine_input = torch.cat([prev_y, current_coarse], dim=1)
        fine_input_proj = self.I_fine(fine_input)
        I_fine_u, I_fine_r, I_fine_e = \
            torch.split(fine_input_proj, self.split_size, dim=1)

        # concatenate for the gates
        # TODO: Simplify all of this business
        I_u = torch.cat([I_coarse_u, I_fine_u], dim=1)
        I_r = torch.cat([I_coarse_r, I_fine_r], dim=1)
        I_e = torch.cat([I_coarse_e, I_fine_e], dim=1)

        # Compute all gates for coarse and fine
        u = F.sigmoid(R_u + I_u + self.bias_u)
        r = F.sigmoid(R_r + I_r + self.bias_r)
        e = F.tanh(r * R_e + I_e + self.bias_e)
        hidden = u * prev_hidden + (1. - u) * e

        # Split the hidden state
        hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)

        # Compute outputs
        out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
        out_fine = self.O4(F.relu(self.O3(hidden_fine)))

        return out_coarse, out_fine, hidden

    def generate(self, seq_len):
        # First split up the biases for the gates
        b_coarse_u, b_fine_u = torch.split(self.bias_u, self.split_size)
        b_coarse_r, b_fine_r = torch.split(self.bias_r, self.split_size)
        b_coarse_e, b_fine_e = torch.split(self.bias_e, self.split_size)

        # Lists for the two output seqs
        c_outputs, f_outputs = [], []

        # Some initial inputs
        out_coarse = Variable(torch.LongTensor([0])).cuda()
        out_fine = Variable(torch.LongTensor([0])).cuda()

        # We'll meed a hidden state
        hidden = self.init_hidden()

        # Need a clock for display
        start = time.time()

        # Loop for generation
        for i in range(seq_len):
            # Split into two hidden states
            hidden_coarse, hidden_fine = \
                torch.split(hidden, self.split_size, dim=1)

            # Scale and concat previous predictions
            out_coarse = out_coarse.unsqueeze(0).float() / 127.5 - 1.
            out_fine = out_fine.unsqueeze(0).float() / 127.5 - 1.
            prev_outputs = torch.cat([out_coarse, out_fine], dim=1)

            # Project input
            coarse_input_proj = self.I_coarse(prev_outputs)
            I_coarse_u, I_coarse_r, I_coarse_e = \
                torch.split(coarse_input_proj, self.split_size, dim=1)

            # Project hidden state and split 6 ways
            R_hidden = self.R(hidden)
            R_coarse_u, R_fine_u, \
            R_coarse_r, R_fine_r, \
            R_coarse_e, R_fine_e = torch.split(R_hidden, self.split_size, dim=1)

            # Compute the coarse gates
            u = F.sigmoid(R_coarse_u + I_coarse_u + b_coarse_u)
            r = F.sigmoid(R_coarse_r + I_coarse_r + b_coarse_r)
            e = F.tanh(r * R_coarse_e + I_coarse_e + b_coarse_e)
            hidden_coarse = u * hidden_coarse + (1. - u) * e

            # Compute the coarse output
            out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
            posterior = F.softmax(out_coarse, dim=1).view(-1)
            distrib = torch.distributions.Categorical(posterior)
            out_coarse = distrib.sample()
            c_outputs.append(out_coarse)

            # Project the [prev outputs and predicted coarse sample]
            coarse_pred = out_coarse.float() / 127.5 - 1.
            fine_input = torch.cat([prev_outputs, coarse_pred.unsqueeze(0)], dim=1)
            fine_input_proj = self.I_fine(fine_input)
            I_fine_u, I_fine_r, I_fine_e = \
                torch.split(fine_input_proj, self.split_size, dim=1)

            # Compute the fine gates
            u = F.sigmoid(R_fine_u + I_fine_u + b_fine_u)
            r = F.sigmoid(R_fine_r + I_fine_r + b_fine_r)
            e = F.tanh(r * R_fine_e + I_fine_e + b_fine_e)
            hidden_fine = u * hidden_fine + (1. - u) * e

            # Compute the fine output
            out_fine = self.O4(F.relu(self.O3(hidden_fine)))
            posterior = F.softmax(out_fine, dim=1).view(-1)
            distrib = torch.distributions.Categorical(posterior)
            out_fine = distrib.sample()
            f_outputs.append(out_fine)

            # Put the hidden state back together
            hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)

            # Display progress
            speed = (i + 1) / (time.time() - start)
            display('Gen: %i/%i -- Speed: %i', (i + 1, seq_len, speed))

        coarse = torch.stack(c_outputs).squeeze(1).cpu().data.numpy()
        fine = torch.stack(f_outputs).squeeze(1).cpu().data.numpy()
        output = combine_signal(coarse, fine)

        return output, coarse, fine

    def init_hidden(self, batch_size=1):
        return (Variable(torch.zeros(batch_size, self.hidden_size)).cuda())

    def print_stats(self):
        parameters = filter(lambda p: p.requires_grad, self.parameters())
        parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
        print('Trainable Parameters: %.3f million' % parameters)